Performance analysis of support vector machine for early identification of citrus diseases

Early citrus disease detection is necessary for optimum citrus productivity. But detecting a citrus disease at an early stage requires expert views or laboratory tests. But getting an expert view of all time is impossible for rural farmers. The present study aimed to create a low-cost, intelligent, affordable citrus disease classification system. This study offered a Support Vector Machine (SVM) based smart classification method for categorizing various citrus diseases. Citrus photos were subjected to a variety of image processing techniques to categorize the diseases using SVM and the kernel. Prior to classification, the images were segmented and the hue channel threshold value was used to differentiate the diseased area from the remaining portion of the image. The segmented image’s color and grey domains were used to extract 13 different texture and color features. This study outlined three different SVM kernel types- Linear, Gaussian, and Polynomial, and evaluated their accuracy and confusion matrix performances. The Radial Based Function with a polynomial kernel derived from the SVM outperformed the SVM's linear and Gaussian kernel

Sulfonated Styrene-(ethylene-co-butylene)-styrene/Montmorillonite Clay Nanocomposites: Synthesis, Morphology, and Properties

Sulfonated styrene-(ethylene-butylene)-styrene triblock copolymer (SSEBS) was synthesized by reaction of acetyl sulfate with SEBS. SSESB-clay nanocomposites were then prepared from hydrophilic Na-montmorillonite (MT) and organically (quaternary amine) modified hydrophobic nanoclay (OMT) at very low loading. SEBS did not show improvement in properties with MT-based nanocomposites. On sulfonation (3 and 6 weight%) of SEBS, hydrophilic MT clay-based nanocomposites exhibited better mechanical, dynamic mechanical, and thermal properties, and also controlled water–methanol mixture uptake and permeation and AC resistance. Microstructure determined by X-ray diffraction, atomic force microscopy, and transmission electron microscopy due to better dispersion of MT nanoclay particles and interaction of MT with SSEBS matrix was responsible for this effect. The resulting nanocomposites have potential as proton transfer membranes for Fuel Cell applications

Japanese Encephalitis—A Pathological and Clinical Perspective

Japanese encephalitis (JE) is the leading form of viral encephalitis in Asia. It is caused by the JE virus (JEV), which belongs to the family Flaviviridae. JEV is endemic to many parts of Asia, where periodic outbreaks take hundreds of lives. Despite the catastrophes it causes, JE has remained a tropical disease uncommon in the West. With rapid globalization and climatic shift, JEV has started to emerge in areas where the threat was previously unknown. Scientific evidence predicts that JEV will soon become a global pathogen and cause of worldwide pandemics. Although some research documents JEV pathogenesis and drug discovery, worldwide awareness of the need for extensive research to deal with JE is still lacking. This review focuses on the exigency of developing a worldwide effort to acknowledge the prime importance of performing an extensive study of this thus far neglected tropical viral disease. This review also outlines the pathogenesis, the scientific efforts channeled into develop a therapy, and the outlook for a possible future breakthrough addressing this killer disease

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

Hindi vowel classification using QCN-MFCC features

In presence of environmental noise, speakers tend to emphasize their vocal effort to improve the audibility of voice. This involuntary adjustment is known as Lombard effect (LE). Due to LE the signal to noise ratio of speech increases, but at the same time the loudness, pitch and duration of phonemes changes. Hence, accuracy of automatic speech recognition systems degrades. In this paper, the effect of unsupervised equalization of Lombard effect is investigated for Hindi vowel classification task using Hindi database designed at TIFR Mumbai, India. Proposed Quantile-based Dynamic Cepstral Normalization MFCC (QCN-MFCC) along with baseline MFCC features have been used for vowel classification. Hidden Markov Model (HMM) is used as classifier. It is observed that QCN-MFCC features have given a maximum improvement of 5.97% and 5% over MFCC features for context-dependent and context-independent cases respectively. It is also observed that QCN-MFCC features have given improvement of 13% and 11.5% over MFCC features for context-dependent and context-independent classification of mid vowels

Auditory ERB like admissible wavelet packet features for TIMIT phoneme recognition

In recent years wavelet transform has been found to be an effective tool for time–frequency analysis. Wavelet transform has been used as feature extraction in speech recognition applications and it has proved to be an effective technique for unvoiced phoneme classification. In this paper a new filter structure using admissible wavelet packet is analyzed for English phoneme recognition. These filters have the benefit of having frequency bands spacing similar to the auditory Equivalent Rectangular Bandwidth (ERB) scale. Central frequencies of ERB scale are equally distributed along the frequency response of human cochlea. A new sets of features are derived using wavelet packet transform's multi-resolution capabilities and found to be better than conventional features for unvoiced phoneme problems. Some of the noises from NOISEX-92 database has been used for preparing the artificial noisy database to test the robustness of wavelet based features